SYSTEM AND METHOD FOR CIRCUIT SIMULATION BASED ON RECURRENT NEURAL NETWORKS

    公开(公告)号:US20190138897A1

    公开(公告)日:2019-05-09

    申请号:US15951052

    申请日:2018-04-11

    Abstract: According to one embodiment of the present invention a circuit simulator configured to simulate a degraded output of a circuit including a plurality of transistors includes: a behavioral recurrent neural network configured to receive an input waveform and to compute a circuit output waveform; a feature engine configured to model one or more degraded circuit elements in accordance with an aging time, to receive the circuit output waveform and to output a plurality of degraded features; and a physics recurrent neural network configured to receive the plurality of degraded features from the feature engine and to simulate the degraded output of the circuit.

    GENERIC HIGH-DIMENSIONAL IMPORTANCE SAMPLING METHODOLOGY

    公开(公告)号:US20180300288A1

    公开(公告)日:2018-10-18

    申请号:US15696150

    申请日:2017-09-05

    Abstract: A method of circuit yield analysis for evaluating rare failure events includes performing initial sampling to detect failed samples respectively located at one or more failure regions in a multi-dimensional parametric space, generating a distribution of failed samples at discrete values along each dimension, identifying the failed samples, performing a transform to project the failed samples into all dimensions in a transform space, and classifying a type of failure region for each dimension in the parametric space.

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